Permanent magnet motor rotor position identification control method
By constructing a nonlinear dynamic model and combining fuzzy neural networks with space vector pulse width modulation, the problems of sensor dependence and insufficient robustness in the permanent magnet motor control system are solved, achieving high-precision rotor position identification and stable control, and reducing system complexity and cost.
Patent Information
- Application Number
- CN202511433231.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-09
- Publication Date
- 2025-12-30
AI Technical Summary
Existing permanent magnet motor control systems rely on mechanical position sensors, which result in high installation costs, weak anti-interference capabilities, and complex maintenance. Furthermore, traditional sensorless control methods suffer from weak signals, insufficient robustness, and susceptibility to chattering at low speeds, and are difficult to handle complex operating conditions such as parameter nonlinearity and sudden load changes.
By constructing a nonlinear dynamic model, combining space vector pulse width modulation and fuzzy neural network, and adopting a dual closed-loop control strategy, the rotor position real-time information is obtained by using the stator current amplitude and phase, and the rotor position is accurately estimated and closed-loop controlled through multi-stage filtering.
It improves rotor position identification accuracy, reduces the impact of noise interference, has strong robustness, simplifies hardware circuitry, reduces cost and maintenance difficulty, meets high-frequency control requirements, and achieves high-precision torque control.
Smart Images
Figure CN121239079A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of permanent magnet motor control technology, and more specifically to a method for rotor position identification and control of a permanent magnet motor. Background Technology
[0002] Permanent magnet motors are widely used in industrial drives, new energy vehicles and other fields due to their high efficiency and high power density. Traditional permanent magnet motor control systems rely on mechanical position sensors, such as encoders and rotary transformers, to obtain rotor position information, which has problems such as high installation cost, weak anti-interference ability and complex maintenance.
[0003] In existing technologies, sensorless control technology indirectly estimates rotor position through motor electromagnetic signals. Sensorless control methods include back EMF method, flux linkage observation method, and sliding mode observation method. However, the back EMF method is susceptible to noise interference due to weak signals at low speeds; the flux linkage observation method relies on the accuracy of motor parameters and lacks robustness; although the sliding mode observation method has strong resistance to parameter changes, it suffers from high-frequency chattering, which affects the accuracy of position identification. In addition, traditional methods often use linear estimation algorithms, which are difficult to handle complex operating conditions such as nonlinear parameters of permanent magnet motors and sudden load changes. Summary of the Invention
[0004] To overcome the aforementioned deficiencies in the prior art, this invention provides a rotor position identification and control method for permanent magnet motors. By constructing a nonlinear dynamic model, the method achieves accurate estimation of the rotor position, combining low noise interference suppression capability with strong robustness, thereby solving the problems existing in the background art.
[0005] This invention provides the following technical solution: a method for rotor position identification and control of a permanent magnet motor, comprising the following steps: Step 1: Set the initial value of the permanent magnet motor rotor position, and use the initial value as the identification benchmark; Step 2: Control the stator current of the permanent magnet motor based on space vector pulse width modulation to make the motor run at a predetermined speed; Step 3: Obtain real-time information on the rotor position by detecting the amplitude and phase of the stator current of the permanent magnet motor; Step 4: Based on the real-time information, a fuzzy neural network is used to identify the rotor position and obtain the rotor position estimate. Step 5: Filter the identified position; the identified position is the rotor position estimate. Step 6: Output the rotor position identification result and perform closed-loop control on the permanent magnet motor.
[0006] Preferably, step one, setting the initial value of the permanent magnet motor rotor position, includes using short-time position sensor signals, back-calculating the initial position based on motor parameters and starting current waveform characteristics through the flux linkage equation, and applying a fixed voltage vector using an open-loop pre-positioning strategy to rotate the rotor to a preset reference position.
[0007] Preferably, the step of controlling the stator current of the permanent magnet motor based on space vector pulse width modulation to make the motor run at a predetermined speed specifically involves: Obtain the synchronous rotation of the permanent magnet synchronous motor The mathematical model in the coordinate system is expressed as: ;in, Indicates stator voltage Axial components, Indicates stator voltage Axial components, Indicates stator resistance. express Shaft inductor, express Shaft inductor, Represents stator current Axial components, Represents stator current Axial components, Represents electric angular velocity. This indicates the magnetic flux linkage of a permanent magnet.
[0008] Preferably, step two employs a dual closed-loop control strategy consisting of an outer speed loop and an inner current loop; for the speed loop, a target speed is set. Generate via PI controller Shaft current command ,Right now ;in, Indicates the speed ring ratio. Indicates the integral coefficient; for the current loop, Shaft current command is Then, through space vector pulse width modulation technology, and It is converted into a three-phase bridge arm switching signal to control the stator current to track the command value.
[0009] Preferably, step three, which obtains real-time rotor position information by detecting the amplitude and phase of the stator current of the permanent magnet motor, specifically involves: exist Stator current vector in coordinate system ,in, The imaginary unit in a complex number; the amplitude is Phase is Due to the electromagnetic torque of the permanent magnet motor ,in, Represents the extreme logarithm. Indicates the rotor position; when hour, At this time, the current phase With rotor position The relationship is represented as: ;in, This indicates the current phase deviation, which is introduced by load torque and parameter errors.
[0010] Preferably, the fuzzy neural network mainly includes an input layer, a hidden layer, and an output layer; the input variables of the input layer are the normalized value of the current amplitude, the current phase, and the motor speed; the hidden layer uses a Gaussian activation function; and the output layer is used to output the rotor position estimate. The input layer is represented as follows: ;in, Indicates input data, This represents the normalized value of the current amplitude. Indicates the phase of the current; Indicates the motor speed; Represents the transpose of a matrix; ; As a normalization benchmark, it represents the rated current, which is the maximum current that the motor is allowed to carry for a long time under rated operating conditions. The Gaussian activation function is expressed as follows: ;in, This represents a Gaussian activation function. Indicates the central parameter, Indicates the width parameter; The output layer uses a linear activation function, and the rotor position estimate is expressed as: ;in, This represents the estimated rotor position. This represents the connection weights from the hidden layer to the output layer. This indicates the number of neurons in the hidden layer. Represents a linear activation function. ; This indicates the output of the hidden layer.
[0011] Preferably, step six specifically includes: The identified position, after filtering in step five, is input to the speed loop and current loop controllers to replace traditional sensor signals, achieving sensorless vector control; in acquiring Shaft voltage command and Shaft voltage command When performing coordinate transformation based on the rotor position estimate and updating the space vector pulse width modulation, the rotor position estimate is used as the rotation angle reference to ensure that the voltage vector accurately acts on the motor air gap magnetic field; the coordinate transformation includes Clarke transformation and Park transformation.
[0012] The technical effects and advantages of this invention are as follows: This invention, through steps two and four, effectively solves the problem of weak back EMF signals at low speeds by integrating SVPWM current control and FNN nonlinear modeling, thus reducing position identification errors. Furthermore, by constructing a fuzzy neural network and employing adaptive parameter adjustment and multi-level filtering, it reduces the impact of load fluctuations, motor parameter temperature drift, and external noise on identification accuracy, exhibiting strong robustness. It also eliminates the need for additional position sensors, simplifying the hardware circuitry, reducing costs and maintenance complexity. The fuzzy neural network algorithm effectively meets high-frequency control requirements and offers good real-time performance; therefore, it effectively improves identification accuracy and real-time performance. Attached Figure Description
[0013] Figure 1 This is a flowchart of the permanent magnet motor rotor position identification and control method of the present invention. Detailed Implementation
[0014] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. In addition, the forms of the various structures described in the following embodiments are merely illustrative. The permanent magnet motor rotor position identification and control method involved in the present invention is not limited to the structures described in the following embodiments. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0015] like Figure 1 As shown, the present invention provides a method for rotor position identification and control of a permanent magnet motor, comprising the following steps: Step 1: Set the initial value of the permanent magnet motor rotor position and use the initial value as the identification benchmark. The purpose is to provide a reliable initial reference benchmark for subsequent position identification, avoid convergence delay or divergence problems caused by initial value deviation, and ensure that the subsequent closed-loop control can operate stably. Step 2: Control the stator current of the permanent magnet motor based on space vector pulse width modulation to make the motor run at a predetermined speed. The purpose is to ensure that the motor runs stably at a predetermined speed through high-precision current control of space vector pulse width modulation technology, so as to provide a stable electromagnetic signal environment for subsequent rotor position detection, and at the same time eliminate the interference of speed fluctuations on position identification. Step 3: By detecting the amplitude and phase of the stator current of the permanent magnet motor, real-time information of the rotor position is obtained. The purpose is to utilize the coupling relationship between the electromagnetic signal of the permanent magnet motor and the rotor position, and use the current amplitude and phase as indirect observations of the rotor position to provide input features for the subsequent fuzzy neural network, thereby realizing real-time monitoring of the rotor position. Step 4: Based on the real-time information, a fuzzy neural network is used to identify the rotor position and obtain the rotor position estimate. The purpose is to use the nonlinear mapping capability of the fuzzy neural network to handle the complex coupling relationship caused by changes in permanent magnet motor parameters, load disturbances and measurement noise, so as to achieve high-precision nonlinear estimation of rotor position and improve the robustness of the identification algorithm. Step 5: Filter the identified position to eliminate noise interference; the identified position is the rotor position estimate; the purpose is to effectively filter out high-frequency noise, current detection error and random fluctuations in the output of the fuzzy neural network through multi-level filtering, improve the smoothness and reliability of the position identification result, and thus meet the real-time requirements of closed-loop control. Step 6: Output the rotor position identification result for closed-loop control of the permanent magnet motor. The purpose is to directly apply the identification result to the closed loop of the control system, forming a complete closed-loop link of "detection-identification-control", so as to achieve precise torque control and stable operation of the permanent magnet motor and eliminate the risk of position sensor failure.
[0016] In this embodiment, it should be specifically explained that step one, setting the initial value of the permanent magnet motor rotor position, includes using short-time position sensor signals, back-calculating the initial position based on motor parameters and starting current waveform characteristics using the flux linkage equation, and applying a fixed voltage vector using an open-loop pre-positioning strategy to rotate the rotor to a preset reference position. Those skilled in the art can choose any method to set the initial value of the permanent magnet motor rotor position. The motor parameters include, but are not limited to, the number of pole pairs and the rated speed. The open-loop pre-positioning strategy means that at the beginning of startup, when the initial rotor position is unknown, the controller actively applies a definite, open-loop voltage vector to forcibly pull and fix the rotor shaft to a known preset position. The open-loop means no feedback adjustment.
[0017] In this embodiment, it should be specifically explained that the control of the stator current of the permanent magnet motor based on space vector pulse width modulation to make the motor run at a predetermined speed specifically involves: Obtain the synchronous rotation of the permanent magnet synchronous motor The mathematical model in the coordinate system is expressed as: ;in, Indicates stator voltage Axial components, Indicates stator voltage Axial components, Indicates stator resistance. express Shaft inductor, express Shaft inductor, Represents stator current Axial components, Represents stator current Axial components, Represents electric angular velocity. Indicates permanent magnet flux linkage; A dual closed-loop control strategy is adopted, consisting of an outer speed loop and an inner current loop; for the speed loop, a target speed is set. Generate via PI controller Shaft current command ,Right now ;in, Indicates the speed ring ratio. Indicates the integral coefficient; for the current loop, Shaft current command is To maximize torque output, space vector pulse width modulation technology is then used to... and The signal is converted into a three-phase bridge arm switch signal to control the stator current to track the command value, thereby achieving precise speed regulation.
[0018] In this embodiment, it should be specifically explained that step three, obtaining real-time rotor position information by detecting the amplitude and phase of the stator current of the permanent magnet motor, specifically involves: exist Stator current vector in coordinate system ,in, The imaginary unit in a complex number; the amplitude is Phase is Due to the electromagnetic torque of the permanent magnet motor ,in, Represents the extreme logarithm. Indicates the rotor position; when hour, At this time, the current phase With rotor position The relationship is represented as: ;in, This indicates the current phase deviation, which is introduced by load torque and parameter errors. The fundamental component of the current vector is extracted by a low-pass filter to eliminate high-frequency harmonic interference and obtain accurate amplitude and phase, thereby obtaining real-time information on the rotor position.
[0019] In this embodiment, it should be specifically noted that the fuzzy neural network mainly includes an input layer, a hidden layer, and an output layer; the input variables of the input layer are the normalized value of the current amplitude, the current phase, and the motor speed; the hidden layer uses a Gaussian activation function; and the output layer is used to output the rotor position estimate. The input layer is represented as follows: ;in, Indicates input data, This represents the normalized value of the current amplitude. Indicates the phase of the current; Indicates the motor speed; Represents the transpose of a matrix; ; As a normalization benchmark, it represents the rated current, which is the maximum current that the motor is allowed to carry for a long time under rated operating conditions. The Gaussian activation function is expressed as follows: ;in, This represents a Gaussian activation function. Indicates the central parameter, This represents the width parameter, which can be adjusted through adaptive learning. The output layer uses a linear activation function, and the rotor position estimate is expressed as: ;in, This represents the estimated rotor position. This represents the connection weights from the hidden layer to the output layer. This indicates the number of neurons in the hidden layer. Represents a linear activation function. ; This indicates the output of the hidden layer.
[0020] In this embodiment, it should be specifically explained that the training process of the fuzzy neural network is as follows: Under typical operating conditions of the motor, data such as actual rotor position, current amplitude, phase, and speed are collected as data samples. These typical operating conditions include 0~1.5 times rated speed and 0~1.2 times rated load. The root mean square error is used as a performance indicator, and the data samples are divided into... The model is divided into training set samples, test set samples, and validation set samples. The fuzzy neural network is trained using the training set samples, the model is validated using the validation set samples, the model performance is monitored and the hyperparameters are adjusted, and finally the model is tested using the test set samples to evaluate the model's generalization ability, thus obtaining the trained fuzzy neural network model. During the model training process, the adaptive inertial weighted particle swarm optimization algorithm can be used to adjust the model parameters to balance the global search and local optimization capabilities.
[0021] In this embodiment, it should be specifically noted that the filtering process for identifying the position in step five uses a second-order generalized integrator phase-locked loop and Kalman filtering; the second-order generalized integrator phase-locked loop performs preliminary filtering on the current phase, extracts a stable phase reference, and suppresses high-frequency noise; the Kalman filter is used to establish the rotor position state equation and observation equation; the Kalman filter is existing technology, and this embodiment will not elaborate on it further.
[0022] In this embodiment, it should be specifically explained that step six is as follows: The identified position, after filtering in step five, is input to the speed loop and current loop controllers to replace traditional sensor signals, achieving sensorless vector control; in acquiring Shaft voltage command and Shaft voltage command When performing coordinate transformation based on the rotor position estimate and updating the space vector pulse width modulation, the rotor position estimate is used as the rotation angle reference to ensure that the voltage vector accurately acts on the motor air gap magnetic field; the coordinate transformation includes Clarke transformation and Park transformation.
[0023] In this embodiment, it should be specifically explained that the physical essence of the current phase deviation is the phase shift of the stator current to compensate for the load torque and parameter deviation after the electromagnetic torque balance is broken. The introduction of load torque and parameter error is specifically manifested in the following way: if load torque exists... To maintain stable rotational speed, the current loop needs to be adjusted. To balance and However, the dynamic response of the current loop exhibits a hysteresis, leading to practical... The deviation from the ideal value causes the current phase to change. Deviation from rotor position During load mutations, The instantaneous increase necessitates an increase in the current loop output to compensate for the torque difference, but due to the presence of inductance, The rise is delayed, resulting in Transient ahead of Under steady-state load, if the motor parameters or There is an error, in order to maintain , The value needs to be non-ideal, resulting in Static deviation occurs; The load torque and parameter error introduced by the above-mentioned load torque and parameter error specifically include permanent magnet flux error, pole pair error and stator inductance error. The permanent magnet flux linkage error is as follows: if the estimated value of the permanent magnet flux linkage is greater than the actual value, the current loop will increase incorrectly to achieve the target torque. ,lead to Being ahead of time, or conversely, leading to Delay; The number of pole pairs affects the relationship between electrical angular velocity and mechanical speed. If the estimated value of the number of pole pairs is not equal to the actual value, the electrical angular velocity command output by the speed loop will have a deviation, which will lead to an error in the angle reference of coordinate transformation when calculating the current phase, thus introducing system deviation. In the stator inductance error Influence Dynamic response speed Affecting cross-coupling terms This leads to current phase tracking misalignment, although when The cross-coupling term is zero at this time, but parameter errors will affect the accuracy of the voltage equation.
[0024] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.
[0025] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.
Claims
1. A method for permanent magnet motor rotor position identification control, characterized in that: The method comprises the following steps: Step one, setting the initial value of the rotor position of the permanent magnet motor, taking the initial value as the reference for identification; Step two, controlling the stator current of the permanent magnet motor based on space vector pulse width modulation, so that the motor runs at a predetermined speed; Step three, obtaining real-time information of the rotor position by detecting the amplitude and phase of the stator current of the permanent magnet motor; Step four, identifying the rotor position by using a fuzzy neural network based on the real-time information, and obtaining the estimated value of the rotor position; Step five, filtering the identified position, i.e., the estimated value of the rotor position; Step six, outputting the identification result of the rotor position, and performing closed-loop control on the permanent magnet motor.
2. The method of claim 1, wherein: The step one of setting the initial value of the rotor position of the permanent magnet motor comprises the following modes: using the signal of a short-time position sensor, inversely deducing the initial position based on the motor parameters and the waveform characteristics of the starting current through the flux equation, and applying a fixed voltage vector to the motor by using an open-loop positioning strategy to make the rotor rotate to a preset reference position.
3. The method of claim 2, wherein: The step two of controlling the stator current of the permanent magnet motor based on space vector pulse width modulation so that the motor runs at a predetermined speed specifically comprises: Obtaining a mathematical model of a permanent magnet synchronous motor in a synchronous rotating coordinate system is represented as: ; wherein denotes the stator voltage axis component, denotes the stator voltage axis component, denotes the stator resistance, denotes axis inductance, denotes axis inductance, denotes the stator current axis component, denotes the stator current axis component, denotes the electrical angular velocity, denotes the permanent magnet flux linkage.
4. The method of claim 3, wherein: Step two employs a dual closed-loop control strategy consisting of an outer speed loop and an inner current loop; for the speed loop, a target speed is set. Generate via PI controller Shaft current command ,Right now ;in, Indicates the speed ring ratio. Indicates the integral coefficient; for the current loop, Shaft current command is Then, through space vector pulse width modulation technology, and It is converted into a three-phase bridge arm switching signal to control the stator current to track the command value.
5. The method of claim 4, wherein: The step three of obtaining real-time information of the rotor position by detecting the amplitude and phase of the stator current of the permanent magnet motor specifically comprises: In The stator current vector where denotes the imaginary unit in complex numbers; the amplitude is and the phase is ; the electromagnetic torque of the permanent magnet machine where denotes the number of pole pairs, denotes the rotor position; when , the current phase is related to the rotor position by ; wherein, represents the current phase deviation, introduced by the load torque and the parameter error.
6. The method of claim 5, wherein: The fuzzy neural network mainly comprises an input layer, a hidden layer, and an output layer; the input variables of the input layer are the normalized value of the current amplitude, the current phase, and the motor speed; the hidden layer adopts a Gaussian activation function; and the output layer is used to output the estimated value of the rotor position. The input layer is represented as: ; wherein, represents the input data, represents the current amplitude normalized value; represents the current phase; represents the motor speed; represents the transpose of a matrix; ; As a reference for normalization, Irrepresents the rated current, i.e. the maximum current that the motor is allowed to pass in the long term under rated operating conditions; The Gaussian-type activation function is represented as: ; where, represents a Gaussian-type activation function, represents a center parameter, represents a width parameter; The output layer employs a linear activation function, the rotor position estimate is represented as: ; where, represents the rotor position estimate, represents the connection weights from the hidden layer to the output layer, represents the number of neurons in the hidden layer, represents the linear activation function, ; represents the hidden layer output.
7. The method of claim 6, wherein: The step six specifically comprises: The identified position after the filtering in step five is input to the speed loop and current loop controller to replace the traditional sensor signal, so as to realize the vector control without position sensor. Shaft voltage command With Shaft voltage command When the rotor position estimation value is obtained, the coordinate transformation is performed based on the rotor position estimation value, and the space vector pulse width modulation is updated, so that the voltage vector is accurately applied to the motor air gap magnetic field with the rotor position estimation value as the rotation angle reference; the coordinate transformation includes Clarke transformation and Park transformation.